In [1]:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import to_hex
import seaborn as sns
import numpy as np
import os
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.manifold import TSNE
from scipy.cluster.hierarchy import linkage, dendrogram
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
In [2]:
df = pd.read_csv('C:/Users/Lympha/Desktop/temp_dir/result_dataframes/wcn_dataframe.csv')
In [3]:
print(df.info)

<bound method DataFrame.info of     Unnamed: 0    pos1:M    pos2:T    pos3:E    pos4:Y    pos5:K    pos6:L  \
0         1A2B  0.613620  0.741194  0.893730       NaN  1.001938  1.142155   
1         1AA9  0.572012  0.712572  0.844383  1.020772  1.049430  1.245395   
2         1AGP  0.509515  0.675077  0.804460  0.939516  1.010788  1.160664   
3         1AM4       NaN       NaN       NaN  0.669556       NaN       NaN   
4         1AN0  0.530860       NaN       NaN       NaN       NaN       NaN   
..         ...       ...       ...       ...       ...       ...       ...   
376       8DNJ  0.658352  0.725105  0.801150  0.951033  1.005913  1.168859   
377       8EBZ       NaN       NaN       NaN       NaN       NaN  0.386689   
378       8EZG  0.532138  0.684146  0.807077  0.955790  1.025675  1.172076   
379       8F0M       NaN       NaN  0.350666       NaN       NaN       NaN   
380       8IJ9  0.358526  0.533275  0.551459  0.663811  0.798362  1.167728   

       pos7:V    pos8:V    pos9:V  ...  pos180:G  pos181:C  pos182:M  \
0    1.160204  1.202924  1.169588  ...       NaN       NaN       NaN   
1    1.269663  1.301629  1.257230  ...       NaN       NaN       NaN   
2    1.191892  1.225350  1.179285  ...       NaN       NaN       NaN   
3    0.678757  0.750341  0.934929  ...       NaN       NaN       NaN   
4         NaN       NaN       NaN  ...       NaN       NaN       NaN   
..        ...       ...       ...  ...       ...       ...       ...   
376  1.185456  1.218883  1.143652  ...       NaN       NaN       NaN   
377       NaN       NaN       NaN  ...       NaN       NaN       NaN   
378  1.178272  1.183252  1.096479  ...       NaN       NaN       NaN   
379       NaN       NaN       NaN  ...       NaN       NaN       NaN   
380  1.201259  1.224167  1.185446  ...       NaN       NaN       NaN   

     pos183:S  pos184:C  pos185:K  pos186:C  pos187:V  pos188:L  pos189:S  
0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
1         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
2         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
3         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
4         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
..        ...       ...       ...       ...       ...       ...       ...  
376       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
377       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
378       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
379       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
380       NaN       NaN       NaN       NaN       NaN       NaN       NaN  

[381 rows x 190 columns]>
In [4]:
print(df.head)

<bound method NDFrame.head of     Unnamed: 0    pos1:M    pos2:T    pos3:E    pos4:Y    pos5:K    pos6:L  \
0         1A2B  0.613620  0.741194  0.893730       NaN  1.001938  1.142155   
1         1AA9  0.572012  0.712572  0.844383  1.020772  1.049430  1.245395   
2         1AGP  0.509515  0.675077  0.804460  0.939516  1.010788  1.160664   
3         1AM4       NaN       NaN       NaN  0.669556       NaN       NaN   
4         1AN0  0.530860       NaN       NaN       NaN       NaN       NaN   
..         ...       ...       ...       ...       ...       ...       ...   
376       8DNJ  0.658352  0.725105  0.801150  0.951033  1.005913  1.168859   
377       8EBZ       NaN       NaN       NaN       NaN       NaN  0.386689   
378       8EZG  0.532138  0.684146  0.807077  0.955790  1.025675  1.172076   
379       8F0M       NaN       NaN  0.350666       NaN       NaN       NaN   
380       8IJ9  0.358526  0.533275  0.551459  0.663811  0.798362  1.167728   

       pos7:V    pos8:V    pos9:V  ...  pos180:G  pos181:C  pos182:M  \
0    1.160204  1.202924  1.169588  ...       NaN       NaN       NaN   
1    1.269663  1.301629  1.257230  ...       NaN       NaN       NaN   
2    1.191892  1.225350  1.179285  ...       NaN       NaN       NaN   
3    0.678757  0.750341  0.934929  ...       NaN       NaN       NaN   
4         NaN       NaN       NaN  ...       NaN       NaN       NaN   
..        ...       ...       ...  ...       ...       ...       ...   
376  1.185456  1.218883  1.143652  ...       NaN       NaN       NaN   
377       NaN       NaN       NaN  ...       NaN       NaN       NaN   
378  1.178272  1.183252  1.096479  ...       NaN       NaN       NaN   
379       NaN       NaN       NaN  ...       NaN       NaN       NaN   
380  1.201259  1.224167  1.185446  ...       NaN       NaN       NaN   

     pos183:S  pos184:C  pos185:K  pos186:C  pos187:V  pos188:L  pos189:S  
0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
1         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
2         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
3         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
4         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
..        ...       ...       ...       ...       ...       ...       ...  
376       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
377       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
378       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
379       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
380       NaN       NaN       NaN       NaN       NaN       NaN       NaN  

[381 rows x 190 columns]>
In [5]:
metadata_df = pd.read_csv('C:/Users/Lympha/Desktop/temp_dir/result_dataframes/metadata_dataframe.csv')


metadata_df.head()
Out[5]:
Unnamed: 0 Title Structure Details Source Organism Taxonomy ID Abstract Method Resolution Original Number of Models Original Number of Chains ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 1A2B HUMAN RHOA COMPLEXED WITH GTP ANALOGUE NaN Homo sapiens 9606 The 2.4-A resolution crystal structure of a do... x-ray diffraction 2.4 1 1 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 1AA9 HUMAN C-HA-RAS(1-171)(DOT)GDP, NMR, MINIMIZED ... NaN Homo sapiens 9606 The backbone 1H, 13C, and 15N resonances of th... solution nmr NaN 1 1 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 1AGP THREE-DIMENSIONAL STRUCTURES AND PROPERTIES OF... C-H-RAS P21 PROTEIN MUTANT WITH GLY 12 REPLACE... Homo sapiens 9606 The three-dimensional structures and biochemic... x-ray diffraction 2.3 1 1 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 1AM4 COMPLEX BETWEEN CDC42HS.GMPPNP AND P50 RHOGAP ... NaN Homo sapiens 9606 Small G proteins transduce signals from plasma... x-ray diffraction 2.7 1 6 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 1AN0 CDC42HS-GDP COMPLEX NaN Homo sapiens 9606 No DOI found x-ray diffraction 2.8 1 2 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 42 columns

In [6]:
plt.figure(figsize=(100,70))
sns.heatmap(df.drop(columns=['Unnamed: 0']), cmap='viridis')
plt.title('WCN on HRAS Experimental Structures')
plt.xlabel('Metric')
plt.ylabel('Structures')
plt.show
Out[6]:
<function matplotlib.pyplot.show(close=None, block=None)>
In [7]:
plt.figure(figsize=(15,10))
sns.histplot(df.drop(columns=['Unnamed: 0']).values.flatten(), bins=50, kde=True)
plt.xlabel('Value')
plt.ylabel('Density')
plt.title('Distribution of Values in feature_dataframe')
plt.show()
In [8]:
nan_percentage = df.isnull().mean() * 100


plt.figure(figsize=(100,70))
sns.barplot(x=nan_percentage.index, y=nan_percentage.values)
plt.xticks(rotation=90)
plt.ylabel('Percentage of NaN values')
plt.title('Percentage of NaN values in each column of feature_dataframe')
plt.show()
In [9]:
merged_nonnorm_df = pd.merge(df, metadata_df[['Unnamed: 0', 'Read Activity Status']], on='Unnamed: 0')

melted_nonnorm = pd.melt(merged_nonnorm_df, id_vars=['Unnamed: 0', 'Read Activity Status'], var_name='Amino Acid Position', value_name='Value')


plt.figure(figsize=(50,40))


sns.violinplot(x="Amino Acid Position", y="Value", hue="Read Activity Status", data=melted_nonnorm, split=True, inner="quart", palette="viridis")


plt.xticks(rotation=90)


plt.title('Violin Plot for All Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Read Activity Status')


plt.tight_layout()


plt.show()
In [10]:
protein_codes = df['Unnamed: 0']


feature_df_numeric = df.drop(columns=['Unnamed: 0'])


scaler = StandardScaler()
feature_normalized = scaler.fit_transform(feature_df_numeric)


feature_normalized_df = pd.DataFrame(feature_normalized, columns=feature_df_numeric.columns)


feature_normalized_df.fillna(0, inplace=True)
In [11]:
plt.figure(figsize=(100,70))
sns.heatmap(feature_normalized_df, cmap="YlGnBu", cbar_kws={'label': 'Z-score'})
plt.title('Heatmap of Normalized WCN dataframe')
plt.show()
In [12]:
plt.figure(figsize=(15,10))
sns.histplot(feature_normalized_df.values.flatten(), bins=50, kde=True)
plt.xlabel('Value')
plt.ylabel('Density')
plt.title('Distribution of Values in WCN dataframe')
plt.show()
In [13]:
merged_df = pd.merge(feature_normalized_df, metadata_df, left_on=protein_codes, right_on="Unnamed: 0")


X = feature_normalized_df
y = metadata_df["Read Activity Status"]
y_factorized = pd.factorize(y)[0]


merged_df.head()
Out[13]:
pos1:M pos2:T pos3:E pos4:Y pos5:K pos6:L pos7:V pos8:V pos9:V pos10:G ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 0.877253 0.822723 1.092406 0.00000 0.112693 0.170658 0.010683 -0.015162 -0.075974 0.192912 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 0.460621 0.549048 0.705383 1.19877 0.547791 0.816144 0.934254 0.970780 1.147667 0.849653 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 -0.165170 0.190533 0.392275 0.58578 0.193777 0.286386 0.278049 0.208847 0.059412 0.291119 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 0.000000 0.000000 0.000000 -1.45079 0.000000 0.000000 -4.051603 -4.535948 -3.352216 0.000000 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 0.048560 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 -3.652692 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 231 columns

In [14]:
melted_data = pd.melt(X.iloc[:, :len(feature_normalized_df.columns)], value_vars=X.iloc[:, :len(feature_normalized_df.columns)].columns)
melted_data['Activity Status'] = np.tile(y, len(X.columns[:len(feature_normalized_df.columns)]))


plt.figure(figsize=(50,40))
sns.violinplot(x="variable", y="value", hue="Activity Status", data=melted_data, split=True, inner="quart", palette="viridis")
plt.xticks(rotation=90)
plt.title('Violin Plot for All Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Activity Status')
plt.tight_layout()
plt.show()
In [15]:
correlation_matrix = X.iloc[:, :len(feature_normalized_df.columns)].corr()


correlation_with_target = X.iloc[:, :len(feature_normalized_df.columns)].apply(lambda x: x.corr(pd.Series(y_factorized)))


plt.figure(figsize=(100, 70))
sns.heatmap(correlation_matrix, cmap="coolwarm", vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'})
plt.title('Correlation Matrix of Amino Acid Positions')
plt.show()


correlation_with_target_abs = correlation_with_target.abs().sort_values(ascending=False)
correlation_with_target_sorted = correlation_with_target[correlation_with_target_abs.index]
correlation_with_target_sorted.head(10)
Out[15]:
pos60:G    0.560507
pos59:A    0.414094
pos58:T    0.338660
pos61:Q    0.306252
pos12:G    0.288751
pos35:T    0.286756
pos65:S    0.226854
pos64:Y    0.226347
pos10:G    0.221812
pos11:A    0.212485
dtype: float64
In [16]:
linked = linkage(feature_normalized, method='ward')


color_map = {
    "active": "red",
    "inactive": "blue"
}


labels = df["Unnamed: 0"].values


plt.figure(figsize=(20,15))
dendro_data = dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=labels)


ax = plt.gca()
xlbls = ax.get_xmajorticklabels()
for lbl in xlbls:
    structure_id = lbl.get_text()
    color = color_map[merged_df[merged_df["Unnamed: 0"] == structure_id]["Read Activity Status"].values[0]]
    lbl.set_color(color)

plt.title('Full Hierarchical Clustering Dendrogram for WCN dataframe(Colored by Activation Status)')
plt.xlabel('Protein Structure Names')
plt.ylabel('Distance (Ward)')
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.show()
In [17]:
merged_df = pd.merge(feature_normalized_df, metadata_df, left_on=protein_codes, right_on="Unnamed: 0")


X = feature_normalized_df
y = merged_df["Read Activity Status"]


merged_df.head()
Out[17]:
pos1:M pos2:T pos3:E pos4:Y pos5:K pos6:L pos7:V pos8:V pos9:V pos10:G ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 0.877253 0.822723 1.092406 0.00000 0.112693 0.170658 0.010683 -0.015162 -0.075974 0.192912 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 0.460621 0.549048 0.705383 1.19877 0.547791 0.816144 0.934254 0.970780 1.147667 0.849653 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 -0.165170 0.190533 0.392275 0.58578 0.193777 0.286386 0.278049 0.208847 0.059412 0.291119 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 0.000000 0.000000 0.000000 -1.45079 0.000000 0.000000 -4.051603 -4.535948 -3.352216 0.000000 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 0.048560 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 -3.652692 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 231 columns

In [18]:
pca = PCA(n_components=2)
principal_components = pca.fit_transform(X.iloc[:, :len(feature_normalized_df.columns)])
colors = {'active': 'red', 'inactive': 'blue'}


pca_df = pd.DataFrame(data=principal_components, columns=['Principal Component 1', 'Principal Component 2'])
pca_df['Activity Status'] = y


explained_variance = pca.explained_variance_ratio_


plt.figure(figsize=(10, 7))
sns.scatterplot(x='Principal Component 1', y='Principal Component 2', hue='Activity Status', data=pca_df, palette=colors)


plt.xlabel(f'Principal Component 1 ({explained_variance[0]*100:.2f}%)')
plt.ylabel(f'Principal Component 2 ({explained_variance[1]*100:.2f}%)')

plt.title('2D PCA of Amino Acid Positions')
plt.show()
In [19]:
pca_3d = PCA(n_components=3)
principal_components_3d = pca_3d.fit_transform(X)


pca_df_3d = pd.DataFrame(data=principal_components_3d, columns=['Principal Component 1', 'Principal Component 2', 'Principal Component 3'])
pca_df_3d['Activity Status'] = y


colors = {'active': 'red', 'inactive': 'blue'}


explained_variance_3d = pca_3d.explained_variance_ratio_


fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pca_df_3d['Principal Component 1'], pca_df_3d['Principal Component 2'], pca_df_3d['Principal Component 3'], c=pca_df_3d["Activity Status"].map(colors), s=50, label=pca_df_3d["Activity Status"].unique())
ax.set_xlabel(f'Principal Component 1 ({explained_variance_3d[0]*100:.2f}%)')
ax.set_ylabel(f'Principal Component 2 ({explained_variance_3d[1]*100:.2f}%)')
ax.set_zlabel(f'Principal Component 3 ({explained_variance_3d[2]*100:.2f}%)')
ax.set_title('3D PCA of Amino Acid Positions')

legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=status, markersize=10, markerfacecolor=colors[status]) for status in colors]
ax.legend(handles=legend_handles, title='Activity Status')

plt.show()
In [20]:
tsne = TSNE(n_components=2, random_state=1)
tsne_2d = tsne.fit_transform(X)


tsne_df = pd.DataFrame(data=tsne_2d, columns=['t-SNE 1', 't-SNE 2'])
tsne_df['Activity Status'] = y


plt.figure(figsize=(10, 7))
sns.scatterplot(x='t-SNE 1', y='t-SNE 2', hue='Activity Status', data=tsne_df, palette={"active": "red", "inactive": "blue"})
plt.title('t-SNE Projection of WCN Data')
plt.show()

C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  warnings.warn(
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  warnings.warn(
In [21]:
label_encoder = LabelEncoder()
y_factorized = label_encoder.fit_transform(y)


rf_clf = RandomForestClassifier(n_estimators=100, random_state=1)
rf_clf.fit(X.iloc[:, :len(feature_normalized_df.columns)], y_factorized)


feature_importances = rf_clf.feature_importances_


importance_df = pd.DataFrame({
    'Amino Acid Position': X.columns[:len(feature_normalized_df.columns)],
    'Importance': feature_importances
})


sorted_importance_df = importance_df.sort_values(by='Importance', ascending=False)


top_n = 15
selected_aminoacids = sorted_importance_df['Amino Acid Position'][:top_n]
sorted_importance_df
Out[21]:
Amino Acid Position Importance
59 pos60:G 0.059047
34 pos35:T 0.040792
58 pos59:A 0.038206
57 pos58:T 0.034480
11 pos12:G 0.031030
... ... ...
174 pos175:D 0.000000
173 pos174:P 0.000000
171 pos172:N 0.000000
169 pos170:K 0.000000
188 pos189:S 0.000000

189 rows × 2 columns

In [22]:
top_features = sorted_importance_df.head(top_n)


colors = cm.Set2(np.linspace(0, 1, top_n))


plt.figure(figsize=(10, 8))
bars = plt.barh(top_features['Amino Acid Position'], top_features['Importance'], color=colors)
plt.gca().invert_yaxis()  # to have the most important feature at the top
plt.title('Top {} Amino Acid Positions by Importance in WCN profile'.format(top_n))
plt.xlabel('Importance')
plt.ylabel('Amino Acid Position')
plt.tight_layout()
plt.show()
In [23]:
colors = cm.Set2(np.linspace(0, 1, len(selected_aminoacids)))


hex_colors = [to_hex(color) for color in colors]


color_dict = dict(zip(selected_aminoacids, hex_colors))


plt.figure(figsize=(15, 10))
for position in selected_aminoacids:
    sns.distplot(X[position], label=position, hist=False, color=color_dict[position])

plt.title('Distribution of Selected Amino Acid Positions in WCN profile')
plt.xlabel('Value')
plt.ylabel('Density')
plt.legend()
plt.show()

C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
In [24]:
selected_correlation_matrix = correlation_matrix.loc[selected_aminoacids, selected_aminoacids]


plt.figure(figsize=(10, 8))
sns.heatmap(selected_correlation_matrix, cmap="coolwarm", annot=True, vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'})
plt.title('Correlation Matrix of Important Amino Acid Positions')
plt.show()
In [25]:
plt.figure(figsize=(10, 6))
for idx, position in enumerate(selected_aminoacids):
    plt.subplot(3, 5, idx+1)
    sns.boxplot(x=y_factorized, y=X[position])
    plt.title(f'{position}:{round(sorted_importance_df.iloc[idx, sorted_importance_df.columns.get_loc("Importance")], 4)}')
    plt.xlabel('Activity Status')
    plt.ylabel('Value')

plt.tight_layout()
plt.show()
In [26]:
melted_data_selected = pd.melt(X[selected_aminoacids], value_vars=selected_aminoacids)
melted_data_selected['Activity Status'] = np.tile(y, len(selected_aminoacids))
colors = {'active': 'red', 'inactive': 'blue'}


plt.figure(figsize=(10, 6))
sns.violinplot(x="variable", y="value", hue="Activity Status", data=melted_data_selected, split=True, inner="quart", palette=colors)
plt.title('Violin Plot for Selected Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Activity Status')
plt.tight_layout()
plt.show()
In [27]:
active_data = X[y == "active"][selected_aminoacids]
inactive_data = X[y == "inactive"][selected_aminoacids]


t_stats = []
p_values = []

for position in selected_aminoacids:
    t_stat, p_value = ttest_ind(active_data[position], inactive_data[position])
    t_stats.append(t_stat)
    p_values.append(p_value)


t_test_results = pd.DataFrame({
    'Amino Acid Position': selected_aminoacids,
    'T-Statistic': t_stats,
    'P-Value': p_values
})

t_test_results
Out[27]:
Amino Acid Position T-Statistic P-Value
59 pos60:G 13.176259 6.675951e-33
34 pos35:T 5.827262 1.205267e-08
58 pos59:A 8.856576 3.227692e-17
57 pos58:T 7.007051 1.118724e-11
11 pos12:G 5.871487 9.444863e-09
9 pos10:G 4.428541 1.242859e-05
60 pos61:Q 6.263025 1.024952e-09
10 pos11:A 4.233321 2.892461e-05
12 pos13:G 2.687185 7.523126e-03
33 pos34:P 0.469921 6.386814e-01
15 pos16:K 3.140513 1.818905e-03
119 pos120:L -3.424862 6.823792e-04
61 pos62:E 1.833182 6.755964e-02
38 pos39:S -2.224189 2.672430e-02
45 pos46:I -2.984954 3.020169e-03
In [28]:
colors = cm.Set2(np.linspace(0, 1, top_n))


fig, ax1 = plt.subplots(figsize=(12, 8))


bars = ax1.bar(t_test_results['Amino Acid Position'], t_test_results['T-Statistic'], color=colors, label='T-Statistic')


ax2 = ax1.twinx()
ax2.scatter(t_test_results['Amino Acid Position'], t_test_results['P-Value'], color='red', marker='o', label='P-Value')
ax2.axhline(y=0.05, color='black', linestyle='--')  # significance threshold


ax2.set_ylim(0, 1)


ax1.set_ylabel('T-Statistic')
ax2.set_ylabel('P-Value', color='red')
ax1.set_xlabel('Amino Acid Position')
ax1.set_title(f'T-Test Results for Top {top_n} Amino Acid Positions')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.tight_layout()
plt.show()
In [29]:
bonferroni_corrected_pvalues = multipletests(t_test_results['P-Value'], method='bonferroni')[1]


fdr_corrected_pvalues = multipletests(t_test_results['P-Value'], method='fdr_bh')[1]


t_test_results['Bonferroni Corrected P-Value'] = bonferroni_corrected_pvalues
t_test_results['FDR Corrected P-Value'] = fdr_corrected_pvalues

t_test_results
Out[29]:
Amino Acid Position T-Statistic P-Value Bonferroni Corrected P-Value FDR Corrected P-Value
59 pos60:G 13.176259 6.675951e-33 1.001393e-31 1.001393e-31
34 pos35:T 5.827262 1.205267e-08 1.807901e-07 3.013168e-08
58 pos59:A 8.856576 3.227692e-17 4.841538e-16 2.420769e-16
57 pos58:T 7.007051 1.118724e-11 1.678085e-10 5.593618e-11
11 pos12:G 5.871487 9.444863e-09 1.416729e-07 2.833459e-08
9 pos10:G 4.428541 1.242859e-05 1.864289e-04 2.663270e-05
60 pos61:Q 6.263025 1.024952e-09 1.537428e-08 3.843569e-09
10 pos11:A 4.233321 2.892461e-05 4.338691e-04 5.423364e-05
12 pos13:G 2.687185 7.523126e-03 1.128469e-01 9.403907e-03
33 pos34:P 0.469921 6.386814e-01 1.000000e+00 6.386814e-01
15 pos16:K 3.140513 1.818905e-03 2.728357e-02 2.728357e-03
119 pos120:L -3.424862 6.823792e-04 1.023569e-02 1.137299e-03
61 pos62:E 1.833182 6.755964e-02 1.000000e+00 7.238532e-02
38 pos39:S -2.224189 2.672430e-02 4.008645e-01 3.083573e-02
45 pos46:I -2.984954 3.020169e-03 4.530254e-02 4.118413e-03
In [30]:
t_test_results.to_clipboard()
In [31]:
colors = cm.Set2(np.linspace(0, 1, top_n))


fig, ax1 = plt.subplots(figsize=(12,8))


bars = ax1.bar(t_test_results['Amino Acid Position'], t_test_results['T-Statistic'], color=colors, label='T-Statistic')


ax2 = ax1.twinx()
ax2.scatter(t_test_results['Amino Acid Position'], t_test_results['FDR Corrected P-Value'], color='red', marker='o', label='FDR Corrected P-Value')
ax2.axhline(y=0.05, color='black', linestyle='--')  # significance threshold


ax2.set_ylim(0, 1)


ax1.set_ylabel('T-Statistic')
ax2.set_ylabel('P-Value', color='red')
ax1.set_xlabel('Amino Acid Position')
ax1.set_title(f'T-Test Results for Top {top_n} Amino Acid Positions in WCN profile')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.tight_layout()
plt.show()